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of host cell splicing
Usama Ashraf, Clara Benoit-Pilven, Vincent Navratil, Cécile Ligneau,
Guillaume Fournier, Sandie Munier, Odile Sismeiro, Jean-Yves Coppée,
Vincent Lacroix, Nadia Naffakh
To cite this version:
Usama Ashraf, Clara Benoit-Pilven, Vincent Navratil, Cécile Ligneau, Guillaume Fournier, et al..
Influenza virus infection induces widespread alterations of host cell splicing. NAR Genomics and
Bioinformatics, Oxford University Press, 2020, 2 (4), �10.1093/nargab/lqaa095�. �hal-03021806�
Influenza virus infection induces widespread
alterations of host cell splicing
Usama Ashraf
1,2,†, Clara Benoit-Pilven
3,4,5,†, Vincent Navratil
6,7,8, C ´ecile Ligneau
1,
Guillaume Fournier
1, Sandie Munier
1, Odile Sismeiro
9, Jean-Yves Copp ´ee
9,
Vincent Lacroix
4,5,*and Nadia Naffakh
1,*1Unit ´e de G ´en ´etique Mol ´eculaire des Virus `a ARN, Institut Pasteur, CNRS UMR3569, Universit ´e de Paris, 75015
Paris, France,2Universit ´e de Paris, Sorbonne Paris Cit ´e, 75013 Paris, France,3Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, 69675 Bron, France,4Laboratoire de Biom ´etrie et Biologie Evolutive, CNRS UMR5558, Universit ´e Lyon 1, 69622 Villeurbanne, France,5EPI ERABLE, INRIA Grenoble Rh ˆone-Alpes, 38330
Montbonnot-Saint-Martin France,6PRABI, Rh ˆone-Alpes Bioinformatics Center, Universit ´e Lyon 1, 69622
Villeurbanne, France,7European Virus Bioinformatics Center, 07743 Jena, Germany,8Institut Franc¸ais de
Bioinformatique, IFB-core, UMS 3601, 91057 ´Evry, France and9Institut Pasteur, P ˆole BIOMICS, Plateforme
Transcriptome et Epigenome, 75015 Paris, France
Received August 01, 2020; Revised September 24, 2020; Editorial Decision October 12, 2020; Accepted November 01, 2020
ABSTRACT
Influenza A viruses (IAVs) use diverse mecha-nisms to interfere with cellular gene expression. Al-though many RNA-seq studies have documented IAV-induced changes in host mRNA abundance, few were designed to allow an accurate quantifica-tion of changes in host mRNA splicing. Here, we show that IAV infection of human lung cells in-duces widespread alterations of cellular splicing, with an overall increase in exon inclusion and de-crease in intron retention. Over half of the mRNAs that show differential splicing undergo no signifi-cant changes in abundance or in their 3 end ter-mination site, suggesting that IAVs can specifically manipulate cellular splicing. Among a randomly se-lected subset of 21 IAV-sensitive alternative splicing events, most are specific to IAV infection as they are not observed upon infection with VSV, induc-tion of interferon expression or inducinduc-tion of an os-motic stress. Finally, the analysis of splicing changes in RED-depleted cells reveals a limited but signifi-cant overlap with the splicing changes in IAV-infected cells. This observation suggests that hijacking of RED by IAVs to promote splicing of the abundant viral NS1 mRNAs could partially divert RED from its
target mRNAs. All our RNA-seq datasets and anal-yses are made accessible for browsing through a user-friendly Shiny interface (http://virhostnet.prabi. fr:3838/shinyapps/flu-splicingorhttps://github.com/ cbenoitp/flu-splicing).
INTRODUCTION
Influenza A viruses (IAVs) cause annual epidemics and oc-casional pandemics with major consequences in terms of mortality and economical loss and are a perennial threat to worldwide public health (1). Their genome consists of eight single-stranded RNA segments of negative polarity, and the virally encoded RNA-dependent RNA polymerase (FluPol) ensures transcription and replication of the viral genome in the nucleus of infected cells. However, viral tran-scription is also critically dependent on the cellular machin-ery of transcription. Notably, initiation of viral mRNA syn-thesis occurs through a unique mechanism known as cap snatching, whereby the FluPol uses short primers derived from capped host RNA polymerase II (PolII) transcripts to prime transcription. Cap snatching is underpinned by a physical association between FluPol and PolII (2). More-over, some viral mRNAs undergo a tightly regulated ing, which involves the host splicing machinery. Many splic-ing factors were identified in proteomic studies or genome-wide loss-of-function genetic screens as being potentially
in-*To whom correspondence should be addressed. Tel: +33 1 45 68 88 11; Fax: +33 1 40 61 32 41; Email: nadia.naffakh@pasteur.fr
Correspondence may also be addressed to Vincent Lacroix. Tel: +33 4 72 43 15 52; Fax: +33 4 72 43 13 88; Email: vincent.lacroix@univ-lyon1.fr †The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
Present addresses:
Clara Benoit-Pilven, Institute of Molecular Medicine, Helsinki University, Helsinki, Finland. Guillaume Fournier, Laboratoire National de Sant´e, Dudelange, Luxembourg.
Nadia Naffakh, Unit´e Biologie des ARN et Virus Influenza, Institut Pasteur, CNRS UMR3569, Paris, France.
C
The Author(s) 2020. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work
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volved in IAV life cycle [e.g. (3,4)]. The RED–SMU1 splic-ing complex was shown to bind FluPol and to promote splicing of the viral NS1 mRNA (5), whereas hnRNPK and NS1-BP are associated with the NS1 viral protein and pro-mote splicing of the viral M1 mRNA (6).
IAVs not only exploit cellular factors to enable the ex-pression of their own genome, but also interfere with the expression of cellular genes in a way that restricts the cel-lular response to viral infection and facilitates viral replica-tion (7). One of the mechanisms involved is the disruption of PolII transcription. IAV-infected cells show a genome-wide reduction of PolII occupancy into gene bodies downstream of the transcription start site, suggesting that cap snatch-ing by FluPol interferes with PolII transcriptional elonga-tion (8). IAV infection was also shown to induce a massive failure of PolII termination at poly(A) sites, leading to ter-mination readthrough and continued transcription in the intragenic regions up to several hundreds of bases down-stream the gene termini (8–10). Other mechanisms of host-cell shut-off involve the viral NS1 protein, which inhibits the post-transcriptional maturation and nuclear export of cellular mRNAs, and the PA-X protein, which causes their degradation in the cytoplasm [reviewed in (7)].
Until recently, there was no evidence for alterations of host splicing in IAV-infected cells. Alternative splicing (AS) expands the diversity of proteins that can be expressed from a given pre-mRNA and can modulate the stability and translation of mRNAs. Although AS is an essential mech-anism for the regulation of gene expression in response to external stimuli, the role of AS in host–pathogen interac-tions has long been underappreciated. This is likely due to the high complexity of AS regulation and the method-ological difficulties of transcriptome-wide analysis of AS. In the recent years, the Illumina RNA-seq technology re-vealed that up to several hundreds of host genes can show altered mRNA splicing upon infection with herpesviruses (11,12), reoviruses (13,14), flaviviruses (15–17) or IAVs (18,19). However, most studies do not use the sequencing depth that is required for accurate quantification of AS iso-form abundance (20), and they provide no or limited vali-dation by an orthogonal methodology.
Here, we aimed at providing a comprehensive analysis of AS alterations induced upon IAV infection of the hu-man alveolar A549 cells. Unlike previously published stud-ies (18,19), we analyzed RNA-seq data using a software that does not rely on existing splice site annotations and there-fore can identify novel splicing events. Our data demon-strate widespread changes in AS events (ASEs) upon viral infection with a trend toward increased splicing of cellular pre-mRNAs. Over half of the mRNAs that show differen-tial splicing undergo no significant changes in abundance or in their 3 end termination site, suggesting that IAVs can specifically manipulate cellular splicing independently of other transcriptional changes. We provide evidence that a substantial proportion of IAV-sensitive ASEs are specific to IAV infection and are conserved across distinct cell lines and viral subtypes. Furthermore, we investigate to what ex-tent hijacking of the RED splicing factor by IAVs to pro-mote splicing of their own mRNAs (5) could account for some of the observed AS changes in infected cells. All our
RNA-seq datasets are available in GEO (accession num-ber GSE154596) and our analyses can be explored through a Shiny user-friendly interface (http://virhostnet.prabi.fr: 3838/shinyapps/flu-splicing).
MATERIALS AND METHODS Cells and viruses
A549 (provided by Prof. M. Schwemmle, Freiburg, Ger-many) and HEK-293T cells (provided by Dr M. Per-ricaudet, Paris, France) were grown in complete Dul-becco’s modified Eagle’s medium (DMEM, Gibco) supple-mented with 10% fetal bovine serum and 1% penicillin– streptomycin (PS). Calu-3 cells (provided by Dr F. Schwalm, Marburg, Germany) were grown in DMEM /F-12 GlutaMax, supplemented with 10% fetal calf serum, 1% PS, 1% sodium pyruvate, 2% sodium bicarbonate and 1% non-essential amino acids. The recombinant A/WSN/33 virus was produced as described in (5). The human sea-sonal IAV A/Paris/1154/2014 (H3N2) was provided by the National Influenza Center at the Institut Pasteur (Paris, France).
RNA-seq
A549 cells plated in 12-well plates (2 × 105 cells/well)
were infected with the WSN virus at a multiplicity of in-fection (MOI) of 5 PFU/cell, or mock infected. Alterna-tively, they were transfected with 25 nM of anti-RED (5 -GUGAUGAGGAGGUGGAUUA-3) or control siRNA (Dharmacon) using the Dharmafect-1 reagent, accord-ing to the manufacturer’s recommendations. At 6 h post-infection or 48 h post-transfection, total RNA was ex-tracted and treated with DNase using the RNeasy Mini Kit (QIAGEN), according to the manufacturer’s instruc-tions. All samples checked on Bioanalyzer RNA6000 Nano Chip (Agilent Technologies, Santa Clara, CA) had an RNA integrity score>9. Starting from 800 ng of DNA-free to-tal RNA from each sample, poly(A)+ RNA purification and library preparation were performed using the TruSeq Stranded mRNA library preparation kit (Illumina, Inc., San Diego, CA), following the manufacturer’s instructions. Libraries were checked for quality on Bioanalyzer DNA chips (Agilent Technologies, Santa Clara, CA). Accurate quantification was performed using the fluorescence-based quantitation Qubit dsDNA HS Assay Kit (Thermo Fisher). Based on a pilot experiment showing that in infected cell samples >50% of the reads aligned with viral sequences, the eight sample libraries derived from four biological repli-cates of mock- and virus-infected cells were randomly dis-tributed into four lanes of a HiSeq2500 sequencer flow cell using a non-equimolar ratio of 1 (mock sample) to 2.3 (infected sample) in each lane, and were sequenced in a paired mode (2 × 120 bases). Raw reads were quality checked using FastQC and mapped to the human genome (hg38, Gencode v27 annotation) and to the A/WSN/33 virus genome (accession numbers CY034132–CY034139) using STAR (v2.5.3a) (21).
Identification of changes in gene expression and termination readthrough
Focusing on the reads that mapped to the human genome, we used HTSeq-count (v0.6.1) (22) to count the number of reads per gene and per intergenic region (up to 5000 nt downstream the annotated transcript end sites of genes). The differential expression analysis as well as the estima-tion of FPKM of genes and their downstream intergenic regions was done with the DESeq2 R package (23) using default parameters. A gene was considered as markedly and significantly differentially expressed if it passed the follow-ing thresholds: gene baseMean 10, gene FPKMIAV-infected1
and/or gene FPKMmock-infected 1, adjusted P-value ≤0.05
and |log2FC|≥ 1. The same thresholds were applied to
com-pare the control siRNA and anti-RED siRNA conditions. The ratio of downstream intergenic FPKM over gene FPKM was computed to estimate the percentage of readthrough (PRT) transcription for each gene in mock and infected samples:
PRTcondition= [downstream of gene FPKMcondition/gene FPKMcondition]. PRT represents the magnitude of the change of
readthrough transcription between the two conditions and was computed as follows: PRT = [PRTIAV-infected
− PRTmock-infected].
A downstream intergenic region was considered as markedly and significantly differentially expressed if it passed the following thresholds: baseMean 10, gene FPKMIAV-infected1 or gene FPKMmock-infected1, adjusted
P-value≤0.05 and 2 ≥ PRT ≥ 0.025.
Identification of changes in alternative splicing
All raw reads were assembled using the KisSplice (v2.4.1) (24) local transcriptome assembler. This tool allows to ex-tract splicing events that correspond to specific patterns in the De Bruijn graph, which we call bubbles. KisS-plice outputs the sequences and quantification of ASEs. The following parameters were used to run KisSplice: – stranded –strandedAbsoluteThreshold 0 –mismatches 2 – counts 2 –min overlap 5 –experimental. The sequences of the ASEs were then mapped to the human genome (hg38 using annotation Gencode v27) using STAR, with de-fault settings. Each event was classified in a type of splic-ing event [alternative acceptor (altA), alternative donor (altD), exon skipping (ES), multiple exon skipping (ES-M) and intron retention (IR)] and assigned to a gene using KisSplice2RefGenome (v1.2.3) (25). Finally, the differen-tial analysis was done with the kissDE R package (v1.1, doi: 10.18129/B9.bioc.kissDE). KissDE outputs three im-portant measures: percent spliced in (PSI), PSI and ad-justed P-value. PSI is a measure representing the percentage of inclusion of an exonic or intronic sequence:
PSI= [inclusion/(inclusion + exclusion)],
where ‘inclusion’ corresponds to the number of reads sup-porting the inclusion isoform and ‘exclusion’ corresponds to the number of reads supporting the exclusion isoform. A PSI of 1 indicates that the exonic or intronic region is always included, while a PSI of 0 indicates that it is always spliced out in the mature RNA.PSI represents the magnitude of
the change of inclusion of the ASE between the two con-ditions and is computed as follows:PSI = [PSIIAV-infected
− PSImock-infected], where PSIIAV-infectedand PSImock-infectedare
the mean PSI values of the four biological replicates. A splicing event was considered as markedly and significantly differentially regulated if it passed the following threshold: |PSI| ≥ 0.10 and adjusted P-value ≤0.05.
To filter out minor isoforms, we used two additional cri-teria. First, we used a threshold on the level of expression of the gene (FPKMIAV-infected≥ 1 and/or FPKMmock-infected≥
1). Second, because this cutoff turned out to be insufficient to filter out splicing variations among minor isoforms of highly expressed genes, we computed a new measure, which we called local event expression (LEE). This measure gives an estimate of the proportion of the isoforms of the gene containing the splicing event. It is computed as follows:
LEE= [RPKsplicing event/RPKgene],
where RPKsplicing event corresponds to the number of reads
per kilobase for the splicing event and RPKgenecorresponds
to the number of reads per kilobase for the full gene. To compute the RPK of the splicing event, we used the num-ber of reads corresponding to the splicing event given by KisSplice and divided it by the effective size of the event (in bp). The effective size corresponds to the number of unique positions where reads, used for the isoform abundance esti-mation, can be aligned. The RPK of the full gene was com-puted using the quantification given by HTSeq-count di-vided by the gene length (in bp). Only the events with LEE ≥ 0.5 in one of the IAV-infected or mock-infected condi-tions were taken into consideration. The same thresholds were applied to compare the control siRNA and anti-RED siRNA conditions.
Principal component and GO term enrichment analyses Principal component analysis (PCA) was performed using ade4 R package (26) on the normalized gene count values, on the normalized intergenic count values or on the PSI values. We plotted coordinates of the eight samples on the first two principal components using the ggplot2 R package (27). We searched for gene ontology (GO) terms enriched in the differentially expressed genes and in the genes contain-ing a differentially spliced event uscontain-ing the topGO R pack-age (doi: 10.18129/B9.bioc.topGO). We used the elim al-gorithm with a user-defined score and tested only the GO terms containing at least 50 genes. For the analysis of the differentially expressed genes, the score was defined as the log2 FC value if the adjusted P-value was lower than the
threshold of 0.05, and 0 if not. This score gave more impor-tance to genes with a large difference of expression between the two conditions. The same principle was used to define the score for the enrichment analysis on the differentially regulated splicing events. The score was defined as |PSI| if the adjusted P-value was lower than the threshold of 0.05, and 0 if not.
RT-PCR and qRT-PCR
A549 and Calu-3 cells were infected at a MOI of 3–5 PFU/cell with the indicated viruses, or mock infected. Al-ternatively, A549 cells were treated with 200 mM KCl or
mock treated for 2 h, or transfected with 10 ng of to-tal RNA prepared from WSN-infected or mock-infected MDCK cells at 6 hpi, using the transfection reagent Lipo-fectamine 3000 (Thermo Fisher). Total RNA was extracted using the RNeasy Mini Kit (Qiagen) following the manu-facturer’s protocol. RT-PCR was performed on 100 ng of total RNA using the forward and reverse primers listed in Supplementary Table S1, and the Superscript III One-Step RT-PCR Kit (Invitrogen) following the manufacturer’s pro-tocol. Amplicons were loaded on a 2% agarose gel. When re-quired, the ImageJ software was used to measure the inten-sity of the bands and calculate aPSI value. The criterion used for validation was |PSI| ≥ 10%. The ES events sub-jected to validation were randomly selected using the ‘Ran-dom’ function in Excel. In the few cases when the design of adequate validation primers turned out to be unfeasible because of the presence of≥2 events sharing the same ge-nomic position, or the presence of a very short (<20 bp) or GC-rich flanking exon, the corresponding ES events were skipped and replaced with another randomly selected ES event.
For IFN, uc.145 and GAPDH qRT-PCR, reverse tran-scription was performed on 500 ng of total RNA, using the Maxima First Strand cDNA Synthesis Kit, which in-cludes a mixture of oligo-dT and random hexamer primers (Thermo Scientific), in a final volume of 20l. Real-time PCR was performed on 2l of a 1:10 dilution of the reverse-transcription reaction, using the Solaris qPCR Master Mix (Thermo Scientific), sets of primers and probe as provided in the Solaris qPCR Gene Expression Assays (Thermo Sci-entific) or, in the case of uc.145, the forward 5-GCAGC GAACCCTGCTAAATA-3 and reverse 5-AGCCGGC ACTAATAGTCCAA-3, primers and the SYBR Green Master Mix (Roche Life Science), with a Light Cycler 480 (Roche).
FACS
A549 (6 × 105) or Calu-3 (24× 105) cells seeded in
six-well plates were mock infected or infected with the indi-cated viruses. Cells were harvested with trypsin, followed by fixation (4% paraformaldehyde) and permeabilization (0.1% Triton X). Cells were then stained with a primary mouse monoclonal anti-influenza virus NP antibody (Ab-cam; ab20343) and a secondary anti-mouse DyLight633 (red) antibody (Thermo Fisher; 35512). The stained cells were subjected to FACS analysis (Attune NxT Flow Cy-tometer). Data were analyzed and processed using the FlowJo software.
Shiny interface
We used RStudio’s Shiny framework to develop a web-based interface that allows to browse the AS, gene expres-sion and readthrough results generated in this study, as well as the multivariate analysis (PCA) of these three tran-scriptional processes. This Shiny interface is available online (http://virhostnet.prabi.fr:3838/shinyapps/flu-splicing) and is also available for download on GitHub to install locally (https://github.com/cbenoitp/flu-splicing).
The users can explore and filter the genes or ASEs of in-terest according to several metrics and can download the
list of selected genes or ASEs as an excel file. The inter-face also gives the possibility to plot some metrics (like
PSI or log2FC) and to download the resulting plot.
Fi-nally, users can browse the intersection of different ASE analyses.
RESULTS
Influenza A/WSN/33 infection induces broad changes in cel-lular splicing
To assess the impact of IAV infection on the AS of host genes, human alveolar A549 cells were mock infected or in-fected with the IAV strain A/WSN/33 (WSN) at a MOI of 5 PFU/cell. The high infection rate in these conditions was assessed by indirect immunofluorescence and FACS analy-sis (∼99% cells positively stained for the viral nucleoprotein; Supplementary Figure S1A). An overview of downstream analyses is provided in Figure 1A. Briefly, poly(A)-tailed RNAs were extracted and subjected to HiSeq2500 Illumina sequencing, and the sequencing reads were mapped to the human genome and the WSN genome using the STAR al-gorithm (21), as detailed in the ‘Materials and Methods’ section. RNA sequencing of control and IAV-infected sam-ples (four independent biological replicates for each condi-tion) yielded 35–80 million paired-end reads (2× 120 nt) mapping to the host genome (Figure1B, blue bars, and Sup-plementary Table S2), allowing for a robust analysis of the transcriptional response to infection. Reads mapping to the viral genome showed the expected distribution across vi-ral open reading frames (Supplementary Figure S1B). The cellular ASEs were analyzed using the KisSplice pipeline (24,25), which enables de novo calling of ASEs and therefore can identify so far non-annotated ASEs. Statistical compar-ison between mock and infected cell samples from the four independent replicates was carried out using the kissDE al-gorithm (25) and the magnitude of the perturbation was quantified using the PSI metric, as described in the ‘Ma-terials and Methods’ section. In addition, the DESeq2 al-gorithm (23) was used for differential gene expression and termination readthrough analysis. PCA indicated that viral infection accounts for 36%, 52% and 54% of the total vari-ance observed in splicing, gene expression and termination readthrough, respectively (Figure1C).
We focused our splicing analysis on the five major types of ASEs, i.e. altA and altD sites, ES, ES-M and IR. To fil-ter out minor isoforms, including minor isoforms derived from highly expressed genes, we computed for each of the
>66 000 ASEs that were identified by kissDE an LEE value
that provides an estimation of the proportion of isoforms showing the ASE (see the ‘Materials and Methods’ section). Only the ASEs showing FPKM≥ 1 and LEE ≥ 0.5 in at least one of the two conditions (IAV-infected and/or mock-infected) were taken into consideration. The resulting ref-erence splicing dataset comprised 25 037 ASEs correspond-ing to 7043 distinct genes (Figure2A, outer circle). Upon additional filtering for ASEs showing a marked and signif-icant change inPSI values (|PSI| ≥ 10% and P ≤ 0.05), we identified 3969 ASEs, corresponding to 2076 genes, that are IAV sensitive (Figure2A, inner circle, and Supplemen-tary Table S3). A substantial proportion of these shows at least one non-annotated splice site (Figure2B), therefore
Figure 1. Dual RNA-seq analysis of IAV-infected cells. (A) Schematic representation of the dual RNA-seq analysis pipeline. Illumina reads corresponding to viral and cellular mRNAs are represented in red and blue, respectively. (B) Mapping of Illumina sequencing reads. The left panel shows the number of reads mapped to the A/WSN/33 virus genome (red), the human genome (blue) or unmapped (gray) for each technical replicate. In the right panel, the same data are shown as percentages of the total number of reads. (C) PCA on PSI values (left panel), normalized gene counts (middle panel) and normalized intergenic counts (right panel). The samples corresponding to each experimental condition (four biological replicates per condition) were plotted on the first two principal components.
Figure 2. Global alterations of the cellular splicing landscape upon IAV infection. (A) Filtering of the ASE dataset. Out of the>66 000 ASEs that were analyzed by kissDE, the 25 037 ASEs showing FPKM≥ 1 and LEE ≥ 0.5 in the mock-infected and/or IAV-infected condition were considered as the reference splicing dataset (outer circle). Upon additional filtering for |PSI| ≥ 10% and P ≤ 0.05, 3969 differentially regulated ASEs were identified (IAV-sensitive splicing dataset, inner circle). (B) Number of annotated (plain bars) and non-annotated (hatched bars) events in the IAV-(IAV-sensitive splicing dataset. The percentage of non-annotated events for each of the indicated types of ASE is indicated above. (C-D) Box plots showing the distribution ofPSI values (PSIIAV-infected− PSImock-infected) (C) or the distribution of the lengths in nucleotides of the variable part (D), for each of the indicated type of ASE within
the IAV-sensitive splicing dataset. The median values are shown as a line in the center of the boxes.
highlighting the added value of performing de novo call-ing of ASEs with KisSplice. A trend for increased exon in-clusion and intron removal in IAV-infected cells, i.e. over-all increased splicing, was observed. Indeed, the median
PSI (PSIIAV-infected− PSImock-infected) value was positive for
ES (+11%) and ES-M events (+10.3%) and negative for IR events (−17.5%) (Figure 2C). The distribution of the lengths of IAV-sensitive ASEs is shown in Figure2D and Supplementary Figure S2. Within the IAV-sensitive dataset, the median length of skipped exons was close to the me-dian length of all exons in the human genome (127 nt ver-sus 149 nt), whereas the median length of retained introns was smaller than the median length of all introns in the hu-man genome (368 nt versus 2036 nt). This observation could partly be due to the limited capacity of KisSplice to assem-ble long introns, since it requires that they are fully covered by reads to be correctly assembled. However, we obtained similar findings using IRFinder, a pipeline dedicated to the analysis of IR (28): the overall decrease in IR was clearly confirmed (median dPSI= −15.4%), and the median length of retained introns in the IAV-sensitive dataset (707 nt) was higher compared with that when the KisSplice pipeline was used but clearly smaller than the median length of all in-trons (Supplementary Figure S3).
IAV-induced changes in cellular splicing are specific and not merely a consequence of interferon induction or cellular stress To assess the specificity of influenza-induced changes in splicing, A549 cells were subjected in parallel to infection with the WSN strain or three distinct treatments: (i) infec-tion with the VSV virus, which unlike IAV does not repli-cate in the nucleus of infected cells; (ii) transfection with RNA extracted from WSN-infected cells, to induce inter-feron expression; and (iii) incubation with 200 mM KCl to induce an osmotic stress. The efficacy of each treatment was controlled by quantifying the IFN mRNA and the uc.145 long noncoding RNA as a marker of the osmotic stress response (29) (Supplementary Figure S4). We focused on ES events because they represent almost 50% of the influenza-sensitive splicing events (Figure2B) and are the most amenable to RT-PCR validation using gene-specific primers. A subset of 21 ES events, randomly selected among the IAV-sensitive events identified by RNA-seq, was charac-terized in the four above-described experimental conditions using RT-PCR. The increase or decrease in exon inclusion expected from the RNA-seq data was confirmed for all 21 ES events; in 15 cases, it was detected exclusively in A549 cells infected with the WSN virus and not in the other ex-perimental conditions (Figure3, left panels, Supplementary
Figure 3. RT-PCR validation and further characterization of IAV-sensitive splicing events detected by RNA-seq. (Left) A549 cells were subjected to viral infection at a high MOI (WSN versus VSV or mock infection), RNA transfection (RNA extracted from WSN-infected versus mock-infected cells) or osmotic stress (200 mM KCl versus mock treatment). (Right) A549 or Calu-3 cells were infected at a high MOI with the WSN virus or a seasonal H3N2 IAV, or mock infected. Total RNA was extracted and RT-PCR was performed using primers flanking the exon of interest. The amplification products were loaded on a 2% agarose gel. The expected size in case of exon inclusion or exclusion is indicated, as well as thePSI value as determined from RNA-seq data. MW: molecular weight. The 300-bp band of the 100-bp DNA Ladder (NEB) is indicated by a star.
Figure S5 and Supplementary Table S4). These results indi-cate that influenza infection induces specific changes in the host AS program that are independent from the interferon response and distinct from a general stress response.
To evaluate to what extent the observed changes in splic-ing were dependent on the experimental virus–cell system, a subset of eight IAV-sensitive ES events identified by RNA-seq was characterized in parallel in A549 or Calu-3 cells infected at a high MOI with the WSN strain (H1N1 sub-type) or with a virus representative of the circulating hu-man seasonal IAVs (H3N2 subtype). Both cell lines were infected at high rates, as assessed by FACS analysis (∼99% and 85% NP-positive A549 cells and∼90% and 80% NP-positive Calu-3 cells upon infection with the WSN and H3N2 viruses, respectively) (Supplementary Figure S1A). The expected increase or decrease in exon inclusion was
de-tected systematically with the WSN and H3N2 viruses in both A549 and Calu-3 cells (Figure3, right panels, Sup-plementary Figure S6 and SupSup-plementary Table S4). Our findings suggest that a substantial proportion of the IAV-induced changes in host splicing detected by RNA-seq are largely conserved across cell lines and shared between dif-ferent influenza viruses.
Overall, RT-PCR validation on RNAs extracted from new batches of mock- or WSN-infected cells was performed on a total of 46 randomly selected ES events showing IAV sensitivity in RNA-seq data. All (including 13 for which at least one splice site was non-annotated) showed the same pattern upon RT-PCR as expected from the RNA-seq data (Supplementary Figures S5–S7 and Supplementary Table S4). Our careful filtering out of minor isoforms likely con-tributes to this high validation rate, which was obtained
respective of whether |PSI| was in the 10–20%, 20–30% or
>30% range (17/17, 12/12 and 17/17 validated events,
re-spectively; Supplementary Table S4), therefore establishing the robustness of our experimental setting and bioinformat-ics pipeline.
IAV-induced changes in cellular splicing are largely indepen-dent from the other cellular transcriptional responses to in-fection
The observed cellular splicing changes in IAV-infected cells (Figure2) could possibly result from other transcriptional changes. Therefore, we examined whether the 2076 genes that show altered splicing upon IAV infection were exhibit-ing changes in the level of expression and/or defects in transcription termination. To this end, we performed DE-Seq2 analysis on both genic and intergenic regions. Upon DESeq2 analysis on genic regions, 5527 genes were found to be differentially expressed (log2 FC ≥ 1, P ≤ 0.05),
among which 2481 and 3046 genes were significantly up-and downregulated in infected cells, respectively (Supple-mentary Table S5). When DESeq2 analysis was performed on intergenic regions that are in a 5-kb window upstream and downstream genic regions, it revealed an overall 2-fold increase in intergenic transcription in WSN-infected cells compared with mock-infected cells (Supplementary Figure S8) in agreement with recently published studies (8–10). Us-ing a PRT metric analogous to PSI (see the ‘Materi-als and Methods’ section), a marked and significant change in transcriptional termination (PRT ≥ 0.025, P ≤ 0.05) was observed for 2012 genes, among which a vast major-ity of 1777 genes showed a positivePRT value, i.e. an in-creased termination readthrough in IAV-infected cells com-pared with mock-infected cells (Supplementary Table S6).
Slightly over 50% of the genes showing differential splic-ing upon IAV infection showed no differential expression or termination readthrough (Figure4A). Besides, GO analysis using the topGO bioinformatics resource (30) revealed dif-ferent enrichment patterns for the genes showing differen-tial splicing, expression or termination readthrough (Figure
4B and Supplementary Figure S8B). Therefore, a large pro-portion of viral-induced alterations of cellular splicing are not merely a side effect of other transcriptional dysregula-tions, and possibly reflect a direct manipulation of the splic-ing machinery in infected cells. To more precisely assess the level of interdependence between splicing and expression changes, we plotted the meanPSI values of IAV-sensitive ES and IR splicing events as a function of the log2FC value
of the corresponding gene (Figure4C, left and right pan-els, respectively). A slightly positive correlation (R2 = 5%,
P = 7 × 10−11) was observed for IR events only (Figure
4C, black curves). When this analysis was restricted to genes involved in the regulation of transcription by PolII, which are significantly enriched among both differentially spliced and differentially expressed genes (Figure4B, indicated by red stars), the observed correlation coefficient was still not significant for ES (R2= 0.3%, P = 0.26) but higher for IR
(R2= 9%, P = 2.2 × 10−3) (Figure4C, red curves).
Over-all, our data establish that splicing and expression changes induced by IAV expression are generally independent from each other. However, they demonstrate a low level of
corre-lation between increased splicing and decreased expression, which is more pronounced for IR than for ES events.
IAV-sensitive and RED protein-controlled splicing events show a limited but significant overlap
As a substantial proportion of IAV-induced changes in splicing appeared to be independent from other global host responses to infection, we hypothesized that some of them could result from a direct interplay between IAV and the splicing machinery. We focused on the RED splicing fac-tor because we previously showed that it is recruited by the influenza polymerase, regulates splicing of the abundant vi-ral NS1 mRNAs and is essential for efficient vivi-ral replica-tion (5). Although RED showed no dramatic changes of its accumulation level or subcellular localization in infected cells (5), its splicing function could possibly be impacted by viral-induced changes, e.g. the nuclear accumulation of viral polymerase and NS1 pre-mRNAs. To investigate this possibility, we performed a global profiling of ASEs con-trolled by RED in A549 cells. Cells were treated with an siRNA targeting RED or a control siRNA (Supplemen-tary Figure S9A). Efficient depletion of RED was achieved at 48 h post-treatment as shown at the RNA (Supplemen-tary Figure S9B) and protein (Supplemen(Supplemen-tary Figure S9C) levels. Poly(A)-tailed RNAs were extracted and subjected to HiSeq2500 Illumina sequencing, and splicing changes induced by RED depletion were analyzed using the same pipeline as described in Figure1A. PCA indicated that the control siRNA had no effect on splicing and that RED de-pletion accounted for 45% of the total variance observed in splicing and up to 63% of the total variance observed in the subset of IR events (Supplementary Figure S10A), in agreement with the specific requirement of RED–SMU1 for the splicing of short introns (31). IAV infection accounted for a lower percentage of the variance (16%) along a clearly separate axis. We identified 11 571 ASEs corresponding to 4570 genes, 14% of which are non-annotated splicing events, that undergo marked (|PSI| ≥ 10%) and significant (P ≤ 0.05) differential regulation upon depletion of RED (Sup-plementary Figure S10B and Sup(Sup-plementary Table S7). A clear trend for decreased exon inclusion and increased IR was observed in RED-depleted cells (Supplementary Fig-ure S10C), in agreement with previously published studies (31,32).
The similarity between the two sets of IAV-sensitive and RED protein-controlled splicing events was limited, and more so for IR than for ES events as expected from the opposite trends observed earlier (i.e. increased versus creased intron removal upon IAV infection and RED de-pletion, respectively; Supplementary Figure S11). We com-pared the observed numbers of ES and IR splicing events that were dysregulated upon both IAV infection and RED depletion, with PSI of same or opposite sign, with the numbers expected under the null hypothesis that the two variables are independent. Interestingly, the observed num-bers were significantly higher than expected for events with
PSI of same sign (Figure 5, blue color) and lower than expected for events withPSI of opposite sign (Figure5, orange color). A Fisher exact test confirmed that the trend was significant for both ES (P< 2 × 10−16) and IR (P= 1.4
Figure 4. Cross-analysis of splicing alterations and other transcriptional changes induced by IAV infection. (A) Venn diagram representing the sets of genes showing differential splicing, expression and/or readthrough upon IAV infection. (B) GO analysis. The top 10 GO terms most enriched among the genes differentially spliced in the CDS (upper panel) or the genes differentially expressed (lower panel) are indicated. The dot size is proportional to the number of genes annotated with the GO term in the full genome, as indicated. (C) Plot representingPSI as a function of the log2FC value, for differentially spliced
ES events (left panel) and IR events (right panel). Each dot represents a distinct splicing event. Regression curves are shown. Black curve: all splicing events. Red dots and curve: splicing events related to genes annotated with the GO term ‘Regulation of transcription by RNA polymerase II’ (GO0006357).
B A
Figure 5. Overlap of IAV-sensitive and RED protein-controlled splicing events. The numbers of ES (A) and IR (B) events that are dysregulated upon both IAV infection and RED depletion are indicated in a chart, and compared with those expected under the null hypothesis that the two variables are independent, according to the sign ofPSI in each condition. Blue color: PSI of same sign; orange color: PSI of opposite sign; gray color: expected numbers.
× 10−3). These findings are indicative of some level of
relat-edness between IAV-sensitive and RED protein-controlled splicing events (further discussed below).
DISCUSSION
Here, we provide an integrated view of changes in the host transcriptome that occur in response to IAV infec-tion, with a focus on IAV-induced changes in splicing that have been documented in only a few studies so far (18,19). Upon RNA-seq analysis of A549 cells infected with the A/WSN/33 virus, we found that >2000 genes show a sig-nificant dysregulation of one or several ASEs at 6 h post-infection. RT-PCR yielded a high validation rate of IAV-sensitive splicing events identified through the KisSplice pipeline (all of the 46 ES events tested). Our findings are consistent with the previous observation of virinduced al-terations of host splicing by Fabozzi et al. (18). The sequenc-ing depth (30M reads per replicate) and number of
repli-cates (two) are lower than in our study, therefore hindering a thorough comparison. When our pipeline for differential splicing was applied to the RNA-seq dataset of Fabozzi et
al., only 95 cellular genes were found to exhibit significant
splicing changes. Out of these 95 genes, only 27 (28%) also showed splicing changes in our dataset, which most likely relates to differences in the experimental protocol and to some degree of dependence on the virus–cell system used (A/Udorn/307/72-Beas2B versus A/WSN/33-A549). Yet, a subset of eight IAV-induced splicing changes from our dataset, which were not present in the dataset of Fabozzi
et al., was consistently observed with two distinct cell lines
and two distinct viruses, suggesting that the overall degree of conservation of IAV-induced splicing changes is actually higher.
Our data reveal an increase in exon inclusion and in-tron removal in IAV-infected cells. We asked whether this is a global trend for increased splicing activity. Such an increased splicing could potentially be related to the fact
that IAV infection strongly interferes with PolII transcrip-tion at the initiatranscrip-tion, elongatranscrip-tion and terminatranscrip-tion stages, in a way that contributes to the shut-off of host gene expres-sion (2,8–10). Indeed, pre-mRNA splicing is tightly cou-pled to PolII transcription. A slowdown of PolII elonga-tion is thought to increase the accessibility of splice sites and therefore to enhance co-transcriptional assembly of the spliceosome (33,34). The splicing and 3 end process-ing of pre-mRNAs are functionally interconnected (34,35). We found only a low level of correlation between increased splicing and decreased expression, which was more pro-nounced for IR than for ES events. This, taken together with the little overlap between the differentially spliced genes and those showing a defect in PolII termination upon IAV infec-tion, suggests that PolII targeting by the virus is not a ma-jor causative mechanism for the observed splicing changes. Similar to our findings, little crossover between the set of genes showing differential splicing and differential expres-sion was also observed in other systems [e.g. (36,37)].
We investigated to what extent IAV-induced splicing changes might indirectly result from the cellular sensing of virus-derived nucleic acids and downstream stimulation of innate immune and inflammatory signaling pathways. It should be noted that A549 cells infected with A/WSN/33 showed no transcriptomic signatures of the interferon re-sponse at 6 h post-infection, in agreement with others’ find-ings and with the strong interferon antagonistic activity of the viral NS1 protein (38). In addition, we assessed whether the osmotic stress response, previously shown to induce PolII termination defects similar to IAV infection (8), also induced similar splicing changes. Among a randomly se-lected subset of 21 IAV-induced splicing changes, only a very minor proportion was triggered in cells subjected to an osmotic shock or infected with the VSV virus. These results suggest that a majority of the observed IAV-induced splic-ing changes are not merely a secondary consequence of a general stress response or the innate immune/inflammatory response caused by infection.
Other mechanisms of viral manipulation of the splic-ing machinery include the inhibition, relocalization and/or post-translational modification of splicing factors, medi-ated by direct or indirect interactions with viral proteins or RNAs [reviewed in (20,39)]. We showed previously that the RED splicing factor, which promotes splicing of the viral NS1 mRNA, is bound by the IAV polymerase (5). Here, we show that the vast majority of IAV-sensitive splicing events are not regulated by RED. Conversely, the silencing of RED induces a wide array of splicing changes, only a fraction of which are recapitulated by IAV infection. Although the overlap between IAV-sensitive and RED-dependent splic-ing events is limited, it is significantly higher than expected under the null hypothesis of full independence, therefore suggesting that IAV-induced changes are partially mediated by RED. A number of factors may explain why IAV infec-tion only partially phenocopies RNA-mediated depleinfec-tion of RED. On the one hand, a 90% knockdown of RED is likely to have a more drastic effect on RED function, as the levels and nuclear localization of RED remain unchanged in IAV-infected cells (5). Viral-induced changes in the nuclear environment of RED may affect its function in a more sub-tle way than depletion, e.g. only a minor fraction of RED
may be bound to the viral polymerase and/or the abundant NS1 mRNA. On the other hand, the transcription slow-down in IAV-infected cells (40) may contribute to the global increase in intron removal we observed and may overcome more subtle effects such as increased IR in a specific sub-set of mRNAs due to altered RED function. Interestingly, in a recent preprint by Thompson et al., an approach simi-lar to ours is used to assess to what extent hnRNP K could be mediating IAV-induced changes in ES events (37). The findings are similar to ours, i.e. the overlap is in the same range whether the IAV infection dataset is compared with the hnRNP K depletion dataset in the study by Thomp-son et al. (21% of ES events are found in common, among which 63% show a concordantPSI) or with the RED de-pletion dataset in our study (35% of ES events are found in common, among which 72% show a concordantPSI; Sup-plementary Figure S11A). These observations support the hypothesis that multiple splicing factors could be involved in the reprogramming of the splicing landscape in IAV-infected cells: RED, hnRNP K and potentially other non-core splicing factors that have been proposed to regulate the splicing of IAV mRNAs, such as SF2 (41) and TRA2A (42). There is evidence that the NS1 protein of IAVs can modu-late host splicing through binding to the U6 snRNA (43) or by inducing a relocalization of the SRSF2 factor (44). The recent finding that NS1 primarily binds intronic sequences (45) might contribute to the marked decrease in IR we ob-served upon IAV infection, also obob-served by Rotival et al. in IAV-infected human macrophages (19). Finally, our RNA-seq data reveal that 74 and 34 genes corresponding to splic-ing factors show differential expression or splicsplic-ing, respec-tively (Supplementary Table S8), which could in turn be the cause of other splicing changes.
Given the magnitude of splicing changes observed at 6 h post-infection, analyses performed at earlier time points could help elucidate the key mechanisms involved. Sepa-rate analysis of poly(A)+ and poly(A)− mRNAs from the cytoplasmic and nuclear fractions would provide a more accurate picture of the splicing landscape and changes in-duced by IAV infection. From a methodological perspec-tive, our RNA-seq datasets provide a valuable basis to train and improve bioinformatic pipelines for the analysis of AS. Indeed, our high-depth sequencing uncovers a large frac-tion of unannotated splice sites, IRs and complex splicing events, whose identification and quantification remain chal-lenging with the currently available softwares. Our datasets also offer opportunities for further investigations aimed at uncovering the functional significance of the splicing alter-ations induced by IAV infection. Notably, genes involved in the regulation of transcription by the cellular PolII were the most enriched among the differentially spliced gene list (Figure4B), which likely points to so far unexplored mech-anisms for viral-induced host shut-off.
SUPPLEMENTARY DATA
Supplementary Dataare available at NARGAB Online.
ACKNOWLEDGEMENTS
The authors wish to thank Leandro Lima for his help in developing a stranded version of KisSplice, and Tim
Krischuns for helpful discussions. This work was performed using the computing facilities of the CC LBBE/PRABI.
FUNDING
French National Research Agency [ANR-16-CE23-0001 to V.L.]; LabEx IBEID [10-LABX-0062 to N.N.]; Horizon 2020––Research and Innovation Framework Programme [665807 to U.A. as a participant in the Pasteur-Paris Uni-versity International PhD Program]; Institut Carnot Pas-teur Microbes & Sant´e [to U.A. as a participant in the Pasteur-Paris University International PhD Program].
Conflict of interest statement. None declared.
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